README.md

metadata

title: MangoMAS — Multi-Agent Cognitive Architecture
colorFrom: yellow
colorTo: red
sdk: gradio
sdk_version: 6.5.1
app_file: app.py
pinned: true
license: mit
tags:
  - mixture-of-experts
  - mcts
  - multi-agent
  - cognitive-architecture
  - neural-routing
  - pytorch
  - reinforcement-learning

MangoMAS — Multi-Agent Cognitive Architecture

An interactive demo of a production-grade multi-agent orchestration platform featuring:

• 10 Cognitive Cells — Biologically-inspired processing units (Reasoning, Memory, Ethics, Causal, Empathy, Curiosity, FigLiteral, R2P, Telemetry, Aggregator)
• MCTS Planning — Monte Carlo Tree Search with policy/value neural networks for task decomposition
• MoE Router — 7M parameter Mixture-of-Experts neural routing gate with 16 expert towers
• Agent Orchestration — Multi-agent task execution with learned routing and weighted aggregation

Architecture

Request → Feature Extractor (64-dim) → RouterNet (MLP) → Expert Selection
                                                              ↓
                                                    [Agent 1, Agent 2, ..., Agent N]
                                                              ↓
                                                    [Cognitive Cell Layer]
                                                              ↓
                                                    Aggregator → Response

Technical Blog Posts

• Building a Neural MoE Router from Scratch
• MCTS for Multi-Agent Task Planning
• Cognitive Cell Architecture Design

Author

Built by Ian Cruickshank — MangoMAS Engineering